An Edge Computing Service Model Based on Information-Centric Networking

Fan, Zhenyu; Wang, Yang; Tian, Kaijin

doi:10.1109/icpads47876.2019.00076

Cited by 6 publications

(2 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Edge computing is initially defined as a new platform that can provide cloud computing functions for mobile terminals in the vicinity of wireless access networks deployed in the mobile device task computing architecture [11,12], and with the expansion of usage scenarios, multiple access devices are also classified as edge computing. Edge computing focuses on the shortcomings of high bandwidth requirements and high latency in cloud computing, and edge devices with capabilities of storage, transmission, and specific computing are deployed at the terminal to assume some of the computing tasks of the central server and realize the offloading of computing [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Design of Hardware Acceleration in Edge Computing Device for Bottle Cap High-Speed Inspection

Liu

Peng

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

High-speed product appearance inspection is crucial for modern automated industrial production such as bottle caps. The main detection method is to capture the image of bottle caps on the high-speed conveyor belt by industrial cameras and send them to the server through edge devices for various analyses. In order to improve production efficiency, it is necessary to increase the inspection rate of bottle caps. However, the transmission rate and huge data throughput of traditional inspection methods limit the inspection rate. Because of the application requirements for improving bottle cap detection efficiency, this paper proposes a hardware acceleration design based on edge devices to improve the bottle cap detection rate significantly. In this paper, an image processing module based on FPGA is designed as the edge device, improving algorithm execution speed through pipeline processing. It realizes the edge detection of the bottle cap and the fast detection of the front or back states and can send the instructions to the actuator to correct it in time when the back bottle cap is detected. It also realizes the positioning of the bottle cap area and cuts the image. Thereby, the amount of data sent to the server is significantly reduced. We have done both functional simulation and hardware implementation. Comparing with the pure software solution, the proposed design reduces the execution time of the algorithm from 16 ms to 3.07 ms, which achieves a more than four times rate increase. The amount of data that needs to be transmitted to the server per second is reduced from 7200 Kb is reduced to 25 Kb, which reduces the transmission capacity and server cache space by more than 280 times compared to the original image. In this paper, the hardware acceleration design of edge devices and the positioning and cropping of original images can greatly reduce the transmission pressure, data calculation pressure, and buffer space requirements of the central server and improve the detection rate. This design can not only be used for bottle cap inspection but also for various machine vision fields, such as defect detection of other workpieces.

show abstract

Section: Introductionmentioning

confidence: 99%

Design of Hardware Acceleration in Edge Computing Device for Bottle Cap High-Speed Inspection

Liu

Peng

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

show abstract

“…Furthermore, with the gradual maturity of architecture design, ICN is envisioned as a promising candidate to support complex interoperability scenarios such as IoT, 5G, or MEC. Considering the practicability and scalability of the ICN scenarios, we implement system management by support with in-network edge computing and a Software Defined Network (SDN), which have been proposed to optimize resource allocation in ICN [42][43][44][45][46][47][48]. Meanwhile, the Standalone Name Resolution (SNR) [49][50][51][52] can be responsible for finding available shards in parallelizing transaction commitments by its registration and resolution functions.…”

mentioning

confidence: 99%

Reputation-Based Sharding Consensus Model in Information-Centric Networking

Shi

Zeng

2022

Electronics

View full text Add to dashboard Cite

The various integration systems of blockchain and information-centric network (ICN) have been applied to provide a trusted and neutral approach to cope with large-scale content distribution in IoT, AR/VR, or 5G/6G scenarios. As a result, the scalability problem of blockchain has been an increasing concern for researchers. The sharding mechanism is recognized as a promising approach to address this challenge. However, there are still many problems in the existing schemes. Firstly, real-time processing speed trades off security of validation. Secondly, simply randomly assigning nodes to the shards may make nodes located very far from each other, which increases the block propagation time and reduces the efficiency advantage brought by the sharding mechanism. Therefore, we optimize a reputation-based sharding consensus model by multi-dimension trust and leverage the affinity propagation (AP) algorithm for gathering consensus nodes into shards. Given the minimal possibility to be at fault in the security of validation, clients can achieve real-time processing speed with assurance. The evaluation results show that the normalized mean square error (NMSE) between the estimated reputation value and the real reputation value of our reputation scheme is less than 0.02. Meanwhile, compared with the classical sharding scheme Omniledger, TPS performance can achieve 1.4 times promotion in the case of a large-scale blockchain network of 1000 nodes.

show abstract